Abstract
We demonstrate FedTSC, a novel federated learning (FL) system for interpretable time series classification (TSC). FedTSC is an FL-based TSC solution that makes a great balance among security, interpretability, accuracy, and efficiency. We achieve this by first extending the concept of FL to consider both stronger security and model interpretability. Then, we propose three novel TSC methods based on explainable features to deal with the challengeable FL problem. To build the model in the FL setting, we propose several security protocols that are well optimized by maximally reducing the bottlenecked communication complexity. We build the FedTSC system based on such a solution, and provide the user Sklearn-like Python APIs for practical utility. We show that the system is easy to use, and the novel TSC approach is superior.
- A. Bagnall, J. Lines, A. Bostrom, J. Large, and E. Keogh. 2017. The Great Time Series Classification Bake Off: a Review and Experimental Evaluation of Recent Algorithmic Advances. Data Mining and Knowledge Discovery 31 (2017), 606--660. Issue 3.Google ScholarDigital Library
- Fangcheng Fu, Yingxia Shao, Lele Yu, Jiawei Jiang, Huanran Xue, Yangyu Tao, and Bin Cui. 2021. VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning. In Proceedings of the 2021 International Conference on Management of Data. 563--576.Google ScholarDigital Library
- Eamonn Keogh and Shruti Kasetty. 2003. On the need for time series data mining benchmarks: a survey and empirical demonstration. Data Mining and knowledge discovery 7, 4 (2003), 349--371.Google Scholar
- Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics. PMLR, 1273--1282.Google Scholar
- Matthew Middlehurst, James Large, Michael Flynn, Jason Lines, Aaron Bostrom, and Anthony Bagnall. 2021. HIVE-COTE 2.0: a new meta ensemble for time series classification. Machine Learning 110, 11 (2021), 3211--3243.Google ScholarDigital Library
- Christoph Molnar. 2022. Interpretable Machine Learning (2 ed.). christophm.github.io/interpretable-ml-book/Google Scholar
- Yuncheng Wu, Shaofeng Cai, Xiaokui Xiao, Gang Chen, and Beng Chin Ooi. [n.d.]. Privacy Preserving Vertical Federated Learning for Tree-based Models. Proceedings of the VLDB Endowment 13, 11 ([n. d.]).Google Scholar
- Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. 2019. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST) 10, 2 (2019), 1--19.Google ScholarDigital Library
Recommendations
Fuzzy rule interpolation based on the ratio of fuzziness of interval type-2 fuzzy sets
Highlights► We present a new method for fuzzy rule interpolation for sparse fuzzy rule-based systems based on the ratio of fuzziness of interval type-2 fuzzy sets. ► It calculates the weights of the closest fuzzy rules with respect ...
AbstractIn recent years, some fuzzy rule interpolation methods have been presented for sparse fuzzy rule-based systems based on interval type-2 fuzzy sets. However, the existing methods have the drawbacks that they cannot guarantee the ...
Overview of Type-2 Fuzzy Logic Systems
Fuzzy set theory has been proposed as a means for modeling the vagueness in complex systems. Fuzzy systems usually employ type-1 fuzzy sets, representing uncertainty by numbers in the range [0, 1]. Despite commercial success of fuzzy logic, a type-1 ...
Comments